OpenRaiser/Intern-Atlas huggingface dataset:
Robust Dynamic Coded Distributed Storage with Partially Storage Constrained Servers
Venue: arXiv cs.IT 2025
Year: 2025

We consider the problem of Robust Dynamic Coded Distributed Storage (RDCDS) with partially storage constrained servers where the goal is to enable robust (resilient to server dropouts) and efficient (as measured by the communication costs) read and update operations, subject to the constraint that the storage at $S$ out of $N$ servers is limited by $1/K_c$ the size of the message. Building upon previously established converse arguments and achievability schemes by Jia et al., in this work we develop a set of new converse arguments and coding designs that enable us to completely characterize the fundamental limits of RDCDS with partially storage constrained servers, i.e., the minimum number of available servers for feasible update operation and the minimum communication costs for read and update operations across various server dropout scenarios.
SkelSplat: Robust Multi-view 3D Human Pose Estimation with Differentiable Gaussian Rendering
Venue: arXiv cs.CV 2025
Year: 2025

Accurate 3D human pose estimation is fundamental for applications such as augmented reality and human-robot interaction. State-of-the-art multi-view methods learn to fuse predictions across views by training on large annotated datasets, leading to poor generalization when the test scenario differs. To overcome these limitations, we propose SkelSplat, a novel framework for multi-view 3D human pose estimation based on differentiable Gaussian rendering. Human pose is modeled as a skeleton of 3D Gaussians, one per joint, optimized via differentiable rendering to enable seamless fusion of arbitrary camera views without 3D ground-truth supervision. Since Gaussian Splatting was originally designed for dense scene reconstruction, we propose a novel one-hot encoding scheme that enables independent optimization of human joints. SkelSplat outperforms approaches that do not rely on 3D ground truth in Human3.6M and CMU, while reducing the cross-dataset error up to 47.8% compared to learning-based methods. Experiments on Human3.6M-Occ and Occlusion-Person demonstrate robustness to occlusions, without scenario-specific fine-tuning. Our project page is available here: https://skelsplat.github.io.
Test-time Diverse Reasoning by Riemannian Activation Steering
Venue: arXiv cs.LG 2025
Year: 2025

Best-of-$N$ reasoning improves the accuracy of language models in solving complex tasks by sampling multiple candidate solutions and then selecting the best one based on some criteria. A critical bottleneck for this strategy is the output diversity limit, which occurs when the model generates similar outputs despite stochastic sampling, and hence recites the same error. To address this lack of variance in reasoning paths, we propose a novel unsupervised activation steering strategy that simultaneously optimizes the steering vectors for multiple reasoning trajectories at test time. At any synchronization anchor along the batch generation process, we find the steering vectors that maximize the total volume spanned by all possible intervened activation subsets. We demonstrate that these steering vectors can be determined by solving a Riemannian optimization problem over the product of spheres with a log-determinant objective function. We then use a Riemannian block-coordinate descent algorithm with a well-tuned learning rate to obtain a stationary point of the problem, and we apply these steering vectors until the generation process reaches the subsequent synchronization anchor. Empirical evaluations on popular mathematical benchmarks demonstrate that our test-time Riemannian activation steering strategy outperforms vanilla sampling techniques in terms of generative diversity and solution accuracy.
Mitigating Negative Flips via Margin Preserving Training
Venue: arXiv cs.CV 2025
Year: 2025

Minimizing inconsistencies across successive versions of an AI system is as crucial as reducing the overall error. In image classification, such inconsistencies manifest as negative flips, where an updated model misclassifies test samples that were previously classified correctly. This issue becomes increasingly pronounced as the number of training classes grows over time, since adding new categories reduces the margin of each class and may introduce conflicting patterns that undermine their learning process, thereby degrading performance on the original subset. To mitigate negative flips, we propose a novel approach that preserves the margins of the original model while learning an improved one. Our method encourages a larger relative margin between the previously learned and newly introduced classes by introducing an explicit margin-calibration term on the logits. However, overly constraining the logit margin for the new classes can significantly degrade their accuracy compared to a new independently trained model. To address this, we integrate a double-source focal distillation loss with the previous model and a new independently trained model, learning an appropriate decision margin from both old and new data, even under a logit margin calibration. Extensive experiments on image classification benchmarks demonstrate that our approach consistently reduces the negative flip rate with high overall accuracy.
A General Ziv-Zakai Bound for DoA Estimation in MIMO Radar Systems
Venue: arXiv cs.IT 2025
Year: 2025

This paper derives a Ziv-Zakai Bound (ZZB) on the Mean Squared Error (MSE) for Direction-of-Arrival (DoA) estimation in co-located Multiple-Input Multiple-Output (MIMO) radar systems and provides closed-form expressions that hold for multi-target scenarios. Unlike classical results that address single-input multiple-output systems with complex Gaussian input signals, the developed ZZB in this paper explicitly accounts for a general input covariance matrix, target radar cross-section statistics and multiple snapshot effects, and admits a compact expression that reveals the dependence of the MSE on the number of transmit antennas, number of targets, Signal-to-Noise Ratio (SNR) and the transmit covariance matrix. Numerical simulations validate the tightness of the ZZB in the a priori dominated region and show how the increase of the number of transmit antennas compresses the threshold SNR for the transition to the Cramer-Rao bound (CRB) while the variation of the number of targets shifts the bound's behavior across SNR regimes. The analytical results and numerical simulations demonstrate that the ZZB is tighter than the CRB, particularly in the low SNR regime.
A CODECO Case Study and Initial Validation for Edge Orchestration of Autonomous Mobile Robots
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous Mobile Robots (AMRs) increasingly adopt containerized micro-services across the Edge-Cloud continuum. While Kubernetes is the de-facto orchestrator for such systems, its assumptions of stable networks, homogeneous resources, and ample compute capacity do not fully hold in mobile, resource-constrained robotic environments. This paper describes a case study on smart-manufacturing AMRs and performs an initial comparison between CODECO orchestration and standard Kubernetes using a controlled KinD environment. Metrics include pod deployment and deletion times, CPU and memory usage, and inter-pod data rates. The observed results indicate that CODECO offers reduced CPU consumption and more stable communication patterns, at the cost of modest memory overhead (10-15%) and slightly increased pod lifecycle latency due to secure overlay initialization.
AI-Powered Data Visualization Platform: An Intelligent Web Application for Automated Dataset Analysis
Venue: arXiv cs.AI 2025
Year: 2025

An AI-powered data visualization platform that automates the entire data analysis process, from uploading a dataset to generating an interactive visualization. Advanced machine learning algorithms are employed to clean and preprocess the data, analyse its features, and automatically select appropriate visualizations. The system establishes the process of automating AI-based analysis and visualization from the context of data-driven environments, and eliminates the challenge of time-consuming manual data analysis. The combination of a Python Flask backend to access the dataset, paired with a React frontend, provides a robust platform that automatically interacts with Firebase Cloud Storage for numerous data processing and data analysis solutions and real-time sources. Key contributions include automatic and intelligent data cleaning, with imputation for missing values, and detection of outliers, via analysis of the data set. AI solutions to intelligently select features, using four different algorithms, and intelligent title generation and visualization are determined by the attributes of the dataset. These contributions were evaluated using two separate datasets to assess the platform's performance. In the process evaluation, the initial analysis was performed in real-time on datasets as large as 100000 rows, while the cloud-based demand platform scales to meet requests from multiple users and processes them simultaneously. In conclusion, the cloud-based data visualization application allowed for a significant reduction of manual inputs to the data analysis process while maintaining a high quality, impactful visual outputs, and user experiences
Why does weak-OOD help? A Further Step Towards Understanding Jailbreaking VLMs
Venue: arXiv cs.CR 2025
Year: 2025

Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed a variety of attack strategies that can successfully bypass the safety mechanisms of VLMs. Among these approaches, jailbreak methods based on the Out-of-Distribution (OOD) strategy have garnered widespread attention due to their simplicity and effectiveness. This paper further advances the in-depth understanding of OOD-based VLM jailbreak methods. Experimental results demonstrate that jailbreak samples generated via mild OOD strategies exhibit superior performance in circumventing the safety constraints of VLMs--a phenomenon we define as ''weak-OOD''. To unravel the underlying causes of this phenomenon, this study takes SI-Attack, a typical OOD-based jailbreak method, as the research object. We attribute this phenomenon to a trade-off between two dominant factors: input intent perception and model refusal triggering. The inconsistency in how these two factors respond to OOD manipulations gives rise to this phenomenon. Furthermore, we provide a theoretical argument for the inevitability of such inconsistency from the perspective of discrepancies between model pre-training and alignment processes. Building on the above insights, we draw inspiration from optical character recognition (OCR) capability enhancement--a core task in the pre-training phase of mainstream VLMs. Leveraging this capability, we design a simple yet highly effective VLM jailbreak method, whose performance outperforms that of SOTA baselines.
Text-based Aerial-Ground Person Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

This work introduces Text-based Aerial-Ground Person Retrieval (TAG-PR), which aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions. Unlike traditional Text-based Person Retrieval (T-PR), which focuses solely on ground-view images, TAG-PR introduces greater practical significance and presents unique challenges due to the large viewpoint discrepancy across images. To support this task, we contribute: (1) TAG-PEDES dataset, constructed from public benchmarks with automatically generated textual descriptions, enhanced by a diversified text generation paradigm to ensure robustness under view heterogeneity; and (2) TAG-CLIP, a novel retrieval framework that addresses view heterogeneity through a hierarchically-routed mixture of experts module to learn view-specific and view-agnostic features and a viewpoint decoupling strategy to decouple view-specific features for better cross-modal alignment. We evaluate the effectiveness of TAG-CLIP on both the proposed TAG-PEDES dataset and existing T-PR benchmarks. The dataset and code are available at https://github.com/Flame-Chasers/TAG-PR.
Demystifying QUIC from the Specifications
Venue: arXiv cs.NI 2025
Year: 2025

QUIC is an advanced transport layer protocol whose ubiquity on the Internet is now very apparent. Importantly, QUIC fuels the next generation of web browsing: HTTP/3. QUIC is a stateful and connection oriented protocol which offers similar features (and more) to the combination of TCP and TLS. There are several difficulties which readers may encounter when learning about QUIC: i.) its rapid evolution (particularly, differentiation between the QUIC standard and the now deprecated Google QUIC), ii.) numerous RFCs whose organization, language, and detail may be challenging to the casual reader, and iii.) the nature of QUIC's cross-layer and privacy-centric implementation, making it impossible to understand or debug by looking at packets alone. For these reasons, the aim of this paper is to present QUIC in a complete yet approachable fashion, thereby demystifying the protocol from its specifications.
TurkEmbed: Turkish Embedding Model on NLI & STS Tasks
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces TurkEmbed, a novel Turkish language embedding model designed to outperform existing models, particularly in Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. Current Turkish embedding models often rely on machine-translated datasets, potentially limiting their accuracy and semantic understanding. TurkEmbed utilizes a combination of diverse datasets and advanced training techniques, including matryoshka representation learning, to achieve more robust and accurate embeddings. This approach enables the model to adapt to various resource-constrained environments, offering faster encoding capabilities. Our evaluation on the Turkish STS-b-TR dataset, using Pearson and Spearman correlation metrics, demonstrates significant improvements in semantic similarity tasks. Furthermore, TurkEmbed surpasses the current state-of-the-art model, Emrecan, on All-NLI-TR and STS-b-TR benchmarks, achieving a 1-4\% improvement. TurkEmbed promises to enhance the Turkish NLP ecosystem by providing a more nuanced understanding of language and facilitating advancements in downstream applications.
Human Motion Intent Inferencing in Teleoperation Through a SINDy Paradigm
Venue: arXiv cs.RO 2025
Year: 2025

Intent inferencing in teleoperation has been instrumental in aligning operator goals and coordinating actions with robotic partners. However, current intent inference methods often ignore subtle motion that can be strong indicators for a sudden change in intent. Specifically, we aim to tackle 1) if we can detect sudden jumps in operator trajectories, 2) how we appropriately use these sudden jump motions to infer an operator's goal state, and 3) how to incorporate these discontinuous and continuous dynamics to infer operator motion. Our framework, called Psychic, models these small indicative motions through a jump-drift-diffusion stochastic differential equation to cover discontinuous and continuous dynamics. Kramers-Moyal (KM) coefficients allow us to detect jumps with a trajectory which we pair with a statistical outlier detection algorithm to nominate goal transitions. Through identifying jumps, we can perform early detection of existing goals and discover undefined goals in unstructured scenarios. Our framework then applies a Sparse Identification of Nonlinear Dynamics (SINDy) model using KM coefficients with the goal transitions as a control input to infer an operator's motion behavior in unstructured scenarios. We demonstrate Psychic can produce probabilistic reachability sets and compare our strategy to a negative log-likelihood model fit. We perform a retrospective study on 600 operator trajectories in a hands-free teleoperation task to evaluate the efficacy of our opensource package, Psychic, in both offline and online learning.
PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises, rules, and conclusions, thereby enabling verification against a target logic. This mechanism mitigates trustworthiness concerns by supporting chain-level validation even in black-box settings. Moreover, PCRLLM facilitates systematic multi-LLM collaboration, allowing intermediate steps to be compared and integrated under formal rules. Finally, we introduce a benchmark schema for generating large-scale step-level reasoning data, combining natural language expressiveness with formal rigor.
Coherence Mechanisms for Provable Self-Improvement
Venue: arXiv cs.LG 2025
Year: 2025

Self-improvement is a critical capability for large language models and other intelligent systems, enabling them to refine their behavior and internal consistency without external supervision. Despite its importance, prior approaches largely rely on empirical heuristics and lack formal guarantees. In this paper, we propose a principled framework for self-improvement based on the concept of \emph{coherence}, which requires that a model's outputs remain consistent under task-preserving transformations of the input. We formalize this concept using projection-based mechanisms that update a baseline model to be coherent while remaining as close as possible to its original behavior. We provide rigorous theoretical guarantees that these mechanisms achieve \emph{monotonic improvement}, measured by a reduction in expected Bregman divergence. Our analysis is comprehensive, covering both \emph{direct} and \emph{two-step} projection methods, and robustly extends these guarantees to non-realizable settings, empirical (finite-sample) distributions, and relaxed coherence constraints. Furthermore, we establish a general \emph{characterization theorem}, showing that any mechanism with similar provable improvement guarantees must inherently conform to a coherence-based structure. This culminates in rigidity results under the demand for universal improvement, establishing coherence as a fundamental and, in a formal sense, necessary principle for provable self-improvement.
Coverage-Guided Pre-Silicon Fuzzing of Open-Source Processors based on Leakage Contracts
Venue: arXiv cs.CR 2025
Year: 2025

Hardware-software leakage contracts have emerged as a formalism for specifying side-channel security guarantees of modern processors, yet verifying that a complex hardware design complies with its contract remains a major challenge. While verification provides strong guarantees, current verification approaches struggle to scale to industrial-sized designs. Conversely, prevalent hardware fuzzing approaches are designed to find functional correctness bugs, but are blind to information leaks like Spectre. To bridge this gap, we introduce a novel and scalable approach: coverage-guided hardware-software contract fuzzing. Our methodology leverages a self-compositional framework to make information leakage directly observable as microarchitectural state divergence. The core of our contribution is a new, security-oriented coverage metric, Self-Composition Deviation (SCD), which guides the fuzzer to explore execution paths that violate the leakage contract. We implemented this approach and performed an extensive evaluation on two open-source RISC-V cores: the in-order Rocket Core and the complex out-of-order BOOM core. Our results demonstrate that coverage-guided strategies outperform unguided fuzzing and that increased microarchitectural coverage leads to a faster discovery of security vulnerabilities in the BOOM core.
One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms
Venue: arXiv cs.LG 2025
Year: 2025

EEG-based emotion recognition is hampered by profound dataset heterogeneity (channel/subject variability), hindering generalizable models. Existing approaches struggle to transfer knowledge effectively. We propose 'One Model for All', a universal pre-training framework for EEG analysis across disparate datasets. Our paradigm decouples learning into two stages: (1) Univariate pre-training via self-supervised contrastive learning on individual channels, enabled by a Unified Channel Schema (UCS) that leverages the channel union (e.g., SEED-62ch, DEAP-32ch); (2) Multivariate fine-tuning with a novel 'ART' (Adaptive Resampling Transformer) and 'GAT' (Graph Attention Network) architecture to capture complex spatio-temporal dependencies. Experiments show universal pre-training is an essential stabilizer, preventing collapse on SEED (vs. scratch) and yielding substantial gains on DEAP (+7.65%) and DREAMER (+3.55%). Our framework achieves new SOTA performance on all within-subject benchmarks: SEED (99.27%), DEAP (93.69%), and DREAMER (93.93%). We also show SOTA cross-dataset transfer, achieving 94.08% (intersection) and 93.05% (UCS) on the unseen DREAMER dataset, with the former surpassing the within-domain pre-training benchmark. Ablation studies validate our architecture: the GAT module is critical, yielding a +22.19% gain over GCN on the high-noise DEAP dataset, and its removal causes a catastrophic -16.44% performance drop. This work paves the way for more universal, scalable, and effective pre-trained models for diverse EEG analysis tasks.
Intuitive control of supernumerary robotic limbs through a tactile-encoded neural interface
Venue: arXiv cs.RO 2025
Year: 2025

Brain-computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile-encoded BCI that leverages sensory afferents through a novel tactile-evoked P300 paradigm, allowing intuitive and reliable decoding of supernumerary motor intentions even when superimposed with voluntary actions. The interface was evaluated in a multi-day experiment comprising of a single motor recognition task to validate baseline BCI performance and a dual task paradigm to assess the potential influence between the BCI and natural human movement. The brain interface achieved real-time and reliable decoding of four supernumerary degrees of freedom, with significant performance improvements after only three days of training. Importantly, after training, performance did not differ significantly between the single- and dual-BCI task conditions, and natural movement remained unimpaired during concurrent supernumerary control. Lastly, the interface was deployed in a movement augmentation task, demonstrating its ability to command two supernumerary robotic arms for functional assistance during bimanual tasks. These results establish a new neural interface paradigm for movement augmentation through stimulation of sensory afferents, expanding motor degrees of freedom without impairing natural movement.
Binary Split Categorical feature with Mean Absolute Error Criteria in CART
Venue: arXiv cs.LG 2025
Year: 2025

In the context of the Classification and Regression Trees (CART) algorithm, the efficient splitting of categorical features using standard criteria like GINI and Entropy is well-established. However, using the Mean Absolute Error (MAE) criterion for categorical features has traditionally relied on various numerical encoding methods. This paper demonstrates that unsupervised numerical encoding methods are not viable for the MAE criteria. Furthermore, we present a novel and efficient splitting algorithm that addresses the challenges of handling categorical features with the MAE criterion. Our findings underscore the limitations of existing approaches and offer a promising solution to enhance the handling of categorical data in CART algorithms.